from __future__ import print_function
import os
import argparse
import torch
import torch.backends.cudnn as cudnn
import numpy as np
from data.config import cfg_mnet as cfg
from layers.functions.prior_box import PriorBox
from utils.nms.py_cpu_nms import py_cpu_nms
import cv2
from models.retinaface import RetinaFace
from utils.box_utils import decode, decode_landm
import time

parser = argparse.ArgumentParser(description='Retinaface')

parser.add_argument('-m', '--trained_model', default='./weights/Final_Retinaface.pth',
                    type=str, help='Trained state_dict file path to open')
parser.add_argument('--cpu', action="store_true", default=False, help='Use cpu inference')
parser.add_argument('--confidence_threshold', default=0.05, type=float, help='confidence_threshold')
parser.add_argument('--top_k', default=5000, type=int, help='top_k')
parser.add_argument('--nms_threshold', default=0.3, type=float, help='nms_threshold')
parser.add_argument('--keep_top_k', default=750, type=int, help='keep_top_k')
parser.add_argument('-v', '--video', default='', help='video file to inference')
parser.add_argument('-s', '--save_image', action="store_true", default=True, help='show detection results')
parser.add_argument('--vis_thres', default=0.6, type=float, help='visualization_threshold')
args = parser.parse_args()


def check_keys(model, pretrained_state_dict):
    ckpt_keys = set(pretrained_state_dict.keys())
    model_keys = set(model.state_dict().keys())
    used_pretrained_keys = model_keys & ckpt_keys
    unused_pretrained_keys = ckpt_keys - model_keys
    missing_keys = model_keys - ckpt_keys
    print('Missing keys:{}'.format(len(missing_keys)))
    print('Unused checkpoint keys:{}'.format(len(unused_pretrained_keys)))
    print('Used keys:{}'.format(len(used_pretrained_keys)))
    assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint'
    return True


def remove_prefix(state_dict, prefix):
    ''' Old style model is stored with all names of parameters sharing common prefix 'module.' '''
    print('remove prefix \'{}\''.format(prefix))
    f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x
    return {f(key): value for key, value in state_dict.items()}


def load_model(model, pretrained_path, load_to_cpu):
    print('Loading pretrained model from {}'.format(pretrained_path))
    if load_to_cpu:
        pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage)
    else:
        device = torch.cuda.current_device()
        pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage.cuda(device))
    if "state_dict" in pretrained_dict.keys():
        pretrained_dict = remove_prefix(pretrained_dict['state_dict'], 'module.')
    else:
        pretrained_dict = remove_prefix(pretrained_dict, 'module.')
    check_keys(model, pretrained_dict)
    model.load_state_dict(pretrained_dict, strict=False)
    return model


if __name__ == '__main__':
    torch.set_grad_enabled(False)
    # net and model
    net = RetinaFace(cfg=cfg, phase="test")
    net = load_model(net, args.trained_model, args.cpu)
    net.eval()
    print('Finished loading model!')
    print(net)
    cudnn.benchmark = True
    device = torch.device("cpu" if args.cpu else "cuda")
    net = net.to(device)

    resize = 1

    # testing begin
    v_f = args.video
    # start to 
    cap = cv2.VideoCapture(v_f)

    if not cap.isOpened():
        print('can not open this video file: {}'.format(v_f))
    
    while (cap.isOpened()):
        ok, frame = cap.read()
        img_raw = frame
        if ok:
            img = np.float32(frame)
            im_height, im_width, _ = img.shape
            scale = torch.Tensor([img.shape[1], img.shape[0], img.shape[1], img.shape[0]])
            img -= (104, 117, 123)
            img = img.transpose(2, 0, 1)
            img = torch.from_numpy(img).unsqueeze(0)
            img = img.to(device)
            scale = scale.to(device)

            tic = time.time()
            loc, conf, landms = net(img)  # forward pass
            cost = time.time() - tic
            print('net forward time: {:.4f}'.format(cost))

            priorbox = PriorBox(cfg, image_size=(im_height, im_width))
            priors = priorbox.forward()
            priors = priors.to(device)
            prior_data = priors.data
            boxes = decode(loc.data.squeeze(0), prior_data, cfg['variance'])
            boxes = boxes * scale / resize
            boxes = boxes.cpu().numpy()
            scores = conf.squeeze(0).data.cpu().numpy()[:, 1]
            landms = decode_landm(landms.data.squeeze(0), prior_data, cfg['variance'])
            scale1 = torch.Tensor([img.shape[3], img.shape[2], img.shape[3], img.shape[2],
                                img.shape[3], img.shape[2], img.shape[3], img.shape[2],
                                img.shape[3], img.shape[2]])
            scale1 = scale1.to(device)
            landms = landms * scale1 / resize
            landms = landms.cpu().numpy()

            # ignore low scores
            inds = np.where(scores > args.confidence_threshold)[0]
            boxes = boxes[inds]
            landms = landms[inds]
            scores = scores[inds]

            # keep top-K before NMS
            order = scores.argsort()[::-1][:args.top_k]
            boxes = boxes[order]
            landms = landms[order]
            scores = scores[order]

            # do NMS
            dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
            keep = py_cpu_nms(dets, args.nms_threshold)
            # keep = nms(dets, args.nms_threshold,force_cpu=args.cpu)
            dets = dets[keep, :]
            landms = landms[keep]

            # keep top-K faster NMS
            dets = dets[:args.keep_top_k, :]
            landms = landms[:args.keep_top_k, :]

            dets = np.concatenate((dets, landms), axis=1)
            # show image
            for b in dets:
                if b[4] < args.vis_thres:
                    continue
                text = "{:.4f}".format(b[4])
                b = list(map(int, b))
                cv2.rectangle(img_raw, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 2)
                cx = b[0]
                cy = b[1] + 12
                cv2.putText(img_raw, text, (cx, cy),
                            cv2.FONT_HERSHEY_DUPLEX, 0.5, (255, 255, 255))

                # landms
                cv2.circle(img_raw, (b[5], b[6]), 4, (0, 0, 255), 4)
                cv2.circle(img_raw, (b[7], b[8]), 4, (0, 255, 255), 4)
                cv2.circle(img_raw, (b[9], b[10]), 4, (255, 0, 255), 4)
                cv2.circle(img_raw, (b[11], b[12]), 4, (0, 255, 0), 4)
                cv2.circle(img_raw, (b[13], b[14]), 4, (255, 0, 0), 4)
            cv2.putText(img_raw, "FPS: {:.3f}".format(1/cost), (10, 30), cv2.FONT_HERSHEY_DUPLEX, 0.7, (0, 255, 0))
            # save image
            cv2.imshow('res', img_raw)
            cv2.waitKey(1)
        else:
            break


        

